Regarding the selection, conventional \({\mathtt {TFC}}\)  measures utilize the variant based on common neighborhood, e.g., Adamic/Adar score [1]}. Furthermore, the selection can be measured by the similarity between node embeddings, e.g., [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, which essentially rely on the factorization of a matrix of the \({\mathtt {TFC}}\)  measure as mentioned in Section REF  [8]}.
Regarding social influence, the related measures mainly focus on the local structure of a user. Burt [9]} proposes to model social influence from cohesion and structural equivalence perspectives. In particular, cohesion describes the direct influence measured by tie strength and the number of influenced friends [10]}, [11]}. The structural equivalence describes the indirect influence between users and is measured by tie embeddedness [12]}.
Recently, many learning-based methods [13]}, [14]}, [15]} are proposed to predict the influence probability based on historical cascades, among which [15]} leverages another psychological concept called conformity to measure the social influence.
